Concepts
Core concepts behind SnitchFeed — listeners, queries, tracked authors, mentions, fit score, intent, intent tags, analytics, and more.
Understanding these concepts will help you get the most out of the MCP integration and the SnitchFeed dashboard.
Listener
A listener is the top-level monitoring unit. It groups keyword queries and/or tracked LinkedIn/X profiles under a single topic and gives that topic a name, color, and AI intent description.
Examples: "Brand Mentions", "Competitor: Acme", "Pain Points in our category".
Each listener has:
| Field | Description |
|---|---|
name | Display label shown in the dashboard and returned by tools |
status | active (running) or paused (suspended, no new mentions collected) |
color | Hex color for visual organization in the dashboard |
intent | Natural language description of the ideal post this listener should match — used to calibrate AI fit scoring |
negative_intent | Description of what irrelevant posts look like — used to push down fit scores for noise |
A listener must always have at least one monitoring source: an active keyword query or a tracked author (or both). Removing the last source requires deleting the listener itself.
Query (Listener Query)
A query is a keyword rule that lives inside a listener. One listener can have multiple queries, which lets you monitor different keyword variations or platform-specific rules under the same topic.
Each query defines:
| Field | Description |
|---|---|
query_text | Boolean keyword expression — see Query Grammar |
platforms | Which platforms this query runs on: reddit, twitter, linkedin, bluesky, hackernews |
whole_word | Match whole words only (default: false) |
case_sensitive | Case-sensitive matching (default: false) |
included_subreddits | Reddit: only scan these subreddits |
excluded_subreddits | Reddit: skip these subreddits |
linkedin_author_job_title | LinkedIn: filter by author job title keyword |
linkedin_author_company | LinkedIn: filter by author company keyword |
linkedin_post_type | LinkedIn: any, person, or company |
twitter_minimum_likes | Twitter: only include posts with at least this many likes |
hackernews_min_points | Hacker News: only include stories with at least this many points |
Queries are not shared across listeners — each belongs to exactly one listener.
Tracked Author (Tracked Profile)
A tracked author (called Tracked Profile in the product UI) is a specific LinkedIn or Twitter/X account (person or company) that a listener follows directly. SnitchFeed ingests what that account posts, without requiring a keyword match. Use tracked authors when monitoring a known account (competitor page, analyst, founder) rather than a topic across the platform.
Tracked authors may live on a listener alongside its queries, or alone on a profile-only listener:
- Resolve the profile URL with
snitchfeed_resolve_tracked_author_profile. - Confirm the resolved account with the user.
- Pass the tool output unchanged into
snitchfeed_create_listenerorsnitchfeed_update_listenerastracked_authors.
To stop following an account, pass its platform and profile_url (from snitchfeed_list_listeners) in remove_tracked_authors. Never reference a tracked author by an internal ID.
Product walkthrough: Tracked Profiles.
Query Grammar
Query text uses a boolean expression language:
| Operator | Example | Meaning |
|---|---|---|
AND | pricing AND saas | Both terms must match |
OR | SnitchFeed OR snitchfeed.com | Either term must match |
NOT | saas NOT enterprise | Excludes matches |
| Parentheses | (saas OR b2b) AND churn | Grouping |
- Bare words match case-insensitively:
react - Quoted strings match exactly:
"React Native" - At least one positive (non-NOT) term is required per query
- LinkedIn queries cannot use
{ }brace expansion,*wildcards,+as AND, or-as NOT
Use the snitchfeed_get_query_grammar tool for the full reference including term limits.
Mention
A mention is a social post or comment that matched one of your active listener queries, or that came from a tracked author on that listener. Mentions are the core output of SnitchFeed.
Each mention includes:
- platform — where it was found (
reddit,twitter,linkedin,bluesky,hackernews) - datePublished — when the post was originally published on the platform
- content — normalized object with:
url,text,author,title,subreddit,parentPost(Reddit comments), and engagement metrics - matched keywords — which query terms triggered the match
- AI analysis —
aiSentiment,aiFitScore,aiIntentTags,aiLanguage,aiIntentSummary - seen — whether the mention has been viewed in the dashboard
Fit Score
Fit score is an AI-assigned relevance score (0–1) that reflects how closely a mention matches the listener's intent description. It answers: "Is this the kind of post we actually care about?"
| Bucket | Score range | Meaning |
|---|---|---|
high | ≥ 0.7 | Strong match for the listener's target audience or topic |
medium | 0.5–0.7 | Partial match — may be worth reviewing |
low | < 0.5 | Weak match — likely noise |
Fit score is calibrated by the listener's intent and negative_intent fields. A listener without intent set will produce less differentiated scores. To improve scoring, set the intent to a specific plain-language description of the ideal post — for example: "SaaS founders actively evaluating social listening tools for their GTM stack."
Intent and Negative Intent
Intent is a free-text field on a listener describing what the ideal matching post or person looks like. The AI uses it to raise fit scores for on-target mentions.
Negative intent is the inverse: a description of posts that should score low even if they keyword-match — for example, "spam, promotional posts, or generic discussions not related to tooling decisions."
Both fields are optional but significantly improve the signal-to-noise ratio in high-volume listeners.
Intent Tags
Intent tags (also called intent signals) are AI-assigned labels on each mention describing its commercial or contextual signal. Multiple tags can apply to a single mention. Use them for filtering, analytics breakdowns, and routing.
| Tag | What it means |
|---|---|
buying_intent | Author is actively evaluating or searching for a product/service to adopt |
recommendation_request | Author is explicitly asking others to recommend tools, vendors, or solutions |
comparison | Author is comparing multiple products or approaches side-by-side |
seeking_alternative | Author is dissatisfied with a current solution and wants to replace it |
content_opportunity | Mention contains a question or discussion that could inspire educational or marketing content |
pain_point | Author is expressing frustration, inefficiency, or a workflow blocker |
competitor_complaint | Author is criticizing a specific competing product or company |
competitor_mention | A competitor is referenced, regardless of sentiment |
feature_request | Author is asking for missing functionality or an integration |
praise | Author is expressing satisfaction or endorsement |
hiring | Mention involves hiring, recruiting, or team expansion |
brand_mention | The monitored organization's own brand or product name appears explicitly |
promotional | Author is actively promoting or marketing a product (launch, affiliate, offer, etc.) |
Sentiment
Sentiment is the AI-detected emotional tone of a mention:
| Value | Score range |
|---|---|
positive | ≥ 0.6 |
neutral | 0.4–0.6 |
negative | < 0.4 |
Sentiment reflects the tone of the post, not its relevance. A high-fit mention can be negative (a competitor complaint) or positive (praise for your product).
Platform
SnitchFeed monitors five social platforms:
| Platform | What is monitored |
|---|---|
reddit | Posts and comments across public subreddits |
twitter | Public tweets (X/Twitter) |
linkedin | Public LinkedIn posts |
bluesky | Public Bluesky posts |
hackernews | Hacker News stories and comments |
Each listener query can be scoped to any combination of platforms. Platform-specific filters (subreddit lists, LinkedIn author filters, Twitter minimum likes, Hacker News minimum points) apply only to their respective platforms.
Analytics
Analytics are aggregated metrics computed over your mention data. You choose:
- Metric —
mentions(count),unique_authors,avg_sentiment,avg_fit_score - Group by — dimension to aggregate across:
day,platform,keyword,sentiment,fitScore,subreddit,language,listener, or intent tag - Breakdown — optional second axis, e.g. daily mentions broken down by platform
- Filters — date range, platform, sentiment, fit score bucket, listener, intent tags, keywords
Analytics answer questions like "How many high-fit mentions did we get last month?" or "Which platform is driving the most buying intent signals?"
Analytics Report
An analytics report is a saved, named analytics query that appears in the SnitchFeed dashboard. Reports run immediately on creation and cache results for 15 minutes, auto-refreshing after that.
Report IDs persist across sessions and can be retrieved with snitchfeed_list_analytics_reports. Use reports for recurring metrics you want visible in the dashboard without re-running queries each time.